DocumentCode
2787995
Title
Neural network modeling of dynamical systems
Author
Bialasiewicz, Jan T. ; Soloway, Don
Author_Institution
Dept. of Electr. Eng. & Comput. Sci., Colorado Univ., Denver, CO, USA
fYear
1990
fDate
5-7 Sep 1990
Firstpage
500
Abstract
A recurrent backpropagation neural network suitable for modelling dynamical systems is analyzed. It is shown that the weight matrices of the neural network model determine, with reasonable accuracy, the impulse response of the modelled dynamical system. By analyzing this impulse response with the eigensystem realization algorithm (ERA), one can obtain state-space representation of the original system. Simulation results are presented
Keywords
linear systems; neural nets; state-space methods; backpropagation neural network; dynamical systems; eigensystem realization algorithm; neural network model; state-space representation; weight matrices; Feedforward neural networks; Linear systems; Mathematical model; NASA; Neural networks; Neurons; Nonlinear equations; Recurrent neural networks; Sampling methods; State-space methods;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control, 1990. Proceedings., 5th IEEE International Symposium on
Conference_Location
Philadelphia, PA
ISSN
2158-9860
Print_ISBN
0-8186-2108-7
Type
conf
DOI
10.1109/ISIC.1990.128503
Filename
128503
Link To Document